Modeling outbreaks of COVID-19 in China: The impact of vaccination and other control measures on curbing the epidemic

Hum Vaccin Immunother. 2024 Dec 31;20(1):2338953. doi: 10.1080/21645515.2024.2338953. Epub 2024 Apr 24.

Abstract

This study aims to examine the development trend of COVID-19 in China and propose a model to assess the impacts of various prevention and control measures in combating the COVID-19 pandemic. Using COVID-19 cases reported by the National Health Commission of China from January 2, 2020, to January 2, 2022, we established a Susceptible-Exposed-Infected-Asymptomatic-Quarantined-Vaccinated-Hospitalized-Removed (SEIAQVHR) model to calculate the COVID-19 transmission rate and Rt effective reproduction number, and assess prevention and control measures. Additionally, we built a stochastic model to explore the development of the COVID-19 epidemic. We modeled the incidence trends in five outbreaks between 2020 and 2022. Some important features of the COVID-19 epidemic are mirrored in the estimates based on our SEIAQVHR model. Our model indicates that an infected index case entering the community has a 50%-60% chance to cause a COVID-19 outbreak. Wearing masks and getting vaccinated were the most effective measures among all the prevention and control measures. Specifically targeting asymptomatic individuals had no significant impact on the spread of COVID-19. By adjusting prevention and control parameters, we suggest that increasing the rates of effective vaccination and mask-wearing can significantly reduce COVID-19 cases in China. Our stochastic model analysis provides a useful tool for understanding the COVID-19 epidemic in China.

Keywords: COVID-19; compartmental model; public health; stochastic.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Basic Reproduction Number
  • COVID-19 Vaccines* / administration & dosage
  • COVID-19* / epidemiology
  • COVID-19* / prevention & control
  • China / epidemiology
  • Disease Outbreaks / prevention & control
  • Humans
  • Incidence
  • Middle Aged
  • SARS-CoV-2* / immunology
  • Vaccination* / statistics & numerical data

Substances

  • COVID-19 Vaccines

Grants and funding

This research was funded by [Hunan Provincial Department of Education] grant number [JG2018B041], [Hunan Natural Fund] grant number [2020JJ4059], [Changsha City Science and Technology Plan Project] grant number [kq2001025] and [Research project of Hunan Provincial Health Commission] grant number [202212054651].